Revisiting Autofocus for Smartphone Cameras
Revisiting Autofocus for Smartphone Cameras
复制标题
重新审视智能手机相机的自动对焦
DOI：
10.1007/978-3-030-01267-0_32
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
M. S. Brown
中科院分区：
文献类型：
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作者：
Abdullah Abuolaim;Abhijith Punnappurath;M. S. Brown
智能手机上的自动对焦（AF）是确定如何移动相机的透镜以使某些场景内容对焦的过程。AF系统使用的基本算法，例如对比度检测和相位差，已经很好地建立起来。然而，确定关于如何最好地聚焦特定场景的高级目标不太清楚。不同的智能手机摄像头采用不同的自动对焦标准，这在一定程度上是显而易见的;例如，有些摄像头试图将中心的物体保持在焦点上，有些则优先考虑面部，而另一些则最大限度地提高整个场景的清晰度。存在不同目标的事实提出了是否存在首选目标的研究问题。当AF应用于动态场景的视频时，这变得更加有趣。本文中的工作旨在重新审视AF的智能手机的时间图像数据的背景下。作为这项工作的一部分，我们描述了一个新的4D数据集的捕获，提供了一个完整的焦点堆栈在每个时间点的时间序列。基于此数据集，我们开发了一个平台和相关的应用程序编程接口（API），模拟真实的AF系统，将透镜运动限制在动态环境和帧捕获的约束范围内。使用我们的平台，我们评估了几个高层次的聚焦目标，并发现了关于用户偏好的有趣见解。我们相信我们新的时间焦点堆栈数据集，AF平台和最初的用户研究结果将有助于推进AF研究。
Autofocus (AF) on smartphones is the process of determining how to move a camera's lens such that certain scene content is in focus. The underlying algorithms used by AF systems, such as contrast detection and phase differencing, are well established. However, determining a high-level objective regarding how to best focus a particular scene is less clear. This is evident in part by the fact that different smartphone cameras employ different AF criteria; for example, some attempt to keep items in the center in focus, others give priority to faces while others maximize the sharpness of the entire scene. The fact that different objectives exist raises the research question of whether there is a preferred objective. This becomes more interesting when AF is applied to videos of dynamic scenes. The work in this paper aims to revisit AF for smartphones within the context of temporal image data. As part of this effort, we describe the capture of a new 4D dataset that provides access to a full focal stack at each time point in a temporal sequence. Based on this dataset, we have developed a platform and associated application programming interface (API) that mimic real AF systems, restricting lens motion within the constraints of a dynamic environment and frame capture. Using our platform we evaluated several high-level focusing objectives and found interesting insight into what users prefer. We believe our new temporal focal stack dataset, AF platform, and initial user-study findings will be useful in advancing AF research.